The existence of forests is essential for our life on Earth. By covering around 31 percent of the world’s total land area, one can say that forests are the backbone of entire ecosystems providing a retreat for countless species of animals and plants. A significant part of the oxygen we breathe is provided by the trees, while they also absorb about 25 percent of greenhouse gases. Also economically we are dependent on forests as the livelihoods of about 1.6 billion people around the world are directly or indirectly connected to forests. Despite these utilities, forestation across the world has faced several challenges ranging from wildfires, human-driven deforestation, poor management and poor conservation in general (UN 2019).

With this project I seek to highlight what has been done by the countries to address these challenges. Therefore I want to figure out and visualize the trends around global reforestation in the last 30 years.

Data

The data of the Global Forest Resources assessment (FAO) contains data on forest development for the intervals between 1990 - 2020, which I will use to answer questions around reforestation and deforestation. Furthermore the FAO is providing a dataset which contains forest area for a few intervals. (FAO 2020).

Main drivers of reforestation

Following plot shows the Top 20 countries with the highest reforestation between 1990 and 2020.

As these results show mainly countries with a huge surface, I want to put the increase of reforestation from 1990-2020 in relation to the forest area in 1990.

This leads to quite surprising results, e.g. that Algeria is one of the countries with the highest reforestation increase given the total forest area in 1990. Nevertheless this doesn’t come out of nowhere, along with other North African countries, Algeria is pursuing several reforestation projects such as the great green wall or “barrage vert” (Göbel 2021). This results in the fact, that Algeria is one of the countries which has a higher forest cover in 2020 than in 1990.

By hovering over the following map, a tooltip with the reforestation increase for each country is shown. The greener the country, the higher is the increase.

Relation between reforestation and deforestation

After we have now an overview on reforestation, I want to answer the question whether governments try to “make up” for their deforestation in the last 30 years.

First of all I want to show the relation of total reforestation and deforestation in the last 30 years, to get a first impression.

There are many outliers with either very high deforestation or reforestation figures. However, such outliers are not surprising when taking a look into recent news. For example Brazil, a country with one of the biggest rainforest areas, has been making negative headlines for years with it’s environment politics (ZEIT 2021).

I will downsize the scale of both axis to have a closer look on the data without the outliers.

The figure shows already a high and not linear distribution of the data. With the Shapiro-Wilk test I want to show the normality of the data.

## 
##  Shapiro-Wilk normality test
## 
## data:  corref$totalref
## W = 0.21089, p-value < 2.2e-16
## 
##  Shapiro-Wilk normality test
## 
## data:  corref$totaldef
## W = 0.16293, p-value < 2.2e-16

The values are below 0.05 for both, reforestation and deforestation, the data significantly deviate from a normal distribution. A result which is already highlighted by the graph.

As the data is therefore not linear, I choose the Spearman method to calculate the correlation.

## [1] "Spearman =  0.540645228525197"

With a value of 0.54 it shows a strong positive correlation, which means, that deforestation has actually an impact on reforestation and a relationship exists.

Additionally, I calculated the power predictive score to highlight the impact of deforestation on reforestation.

##          x        y            result_type        pps metric baseline_score
## 1 totaldef totalref predictive power score 0.13996059    MAE       1510.349
## 2 totalref totaldef predictive power score 0.09232351    MAE       2406.051
##   model_score cv_folds seed algorithm model_type
## 1    1321.497        5    1      tree regression
## 2    2281.094        5    1      tree regression

However this value is with 0.14 not as high as expected after the correlation result.